Distance, Transaction Costs, and Preferences in European Trade

Abstract

Beckerman (1956) and Linder (1961) have suggested that international trade is not determined by supply-side factors alone—perceptions about foreign countries and country preferences matter. We explore the relation between exports, cultural distance, and country preferences in Europe. The results show that several distance and preference-related variables, based on Hofstede’s cultural dimensions, income gaps, and voting patterns in the Eurovision Song Contest, are significantly related to bilateral trade. We conclude that cultural distance and preferences influence trade through several channels, both indirectly through transaction costs and more directly, as countries seem to prefer some trade partners before others.

• international trade
• psychic distance
• country preferences
• Eurovision
• gravity model

Notes

¹In fact, some researchers, like Ghemawat (2001), suggest that cultural distance is important mainly because it affects consumer preferences.

²Since 2009, the national vote has been decided by a 50/50 combination of televoting and jury votes.

³See http://www.eurovision.tv/page/news?id=3533&_t=previous_record_crushed_10680682_televotes (accessed November 30, 2012).

⁴Interestingly, there seems to be a lack of research on who actually takes part in the televoting process. Determining the demographics of televoters is complicated, partly because individuals are allowed to cast multiple votes.

⁵See, e.g., Melitz (2003), Chaney (2008), Baldwin and Taglioni (2006), and Helpman et al. (2008).

⁶One weakness of this variable is that it does not fully reflect the highest and lowest country preferences. In the extreme case where all other countries have a strong preference for country i, it will not be recorded as a country preference, but rather as high song quality (since the average score for country i will be high). Similarly, the variable will not capture instances where all other countries dislike country i, since it will have a low average score. However, no country has received the highest score from all contestants and it is rare that a country receives a zero score from all others: since 1975, that has only happened about a dozen times.

⁷Other approaches to control for MTR include a two-step approach suggested by Anderson and Van Wincoop (2003) that solves for MTR as a function of observables, and an alternative including the calculation of a GDP-weighted remoteness index or a fixed effects regression approach (Feenstra 2002; 2004). For a discussion, see also Benedidtis and Vicarelli (2009).

⁸As an alternative, Baldwin and Taglioni (2006) note that the inclusion of simple country dummies removes part of the cross-section correlation between unobservables and the right-hand-side variables, but leaves the time-series correlation and country-pair effects. Yet much country-specific information is lost through the use of country dummies. See also Benedicitis and Vicarelli (2009) for a discussion on fixed effects and gravity model estimation.

⁹It should be noted that several researchers have recently contested the FEVD model (Greene 2011a; Greene 2011b; Breusch et al. 2011a; Breusch et al. 2011b) because of its asymptotic properties and because it may underestimate standard errors. Hence, we do not base our analysis on the FEVD model but instead include FEVD estimations in order to explore how unobserved heterogeneity and firm-level fixed effects influence the results.

¹⁰For example, Greenaway et al. (2008) estimate a firm-level gravity model analyzing trade within the food industry and follow the now well-established route of including population as a proxy for factor prices.

¹¹We also use time-varying country effects (MTR) with different frequencies.

¹²For a description of the average score received by EU15 countries, see Figure A1. Table A3 shows that trade within the EU15 region and the Eastern European bloc is higher than trade between the blocs, while Table A4 shows that both Eastern European countries and EU15 countries give somewhat higher scores to countries from their own region. Table A5 identifies the countries we include in the Eastern European bloc.

FIGURE A1 Average EU15 ESC score 1975–2005. Note: Calculation based on regression estimation sample.